1. Field of the Invention
This invention is generally related to adaptive filtering of digital image signals representing the arrayed pixels of a digitized image. The invention has particular, but not exclusive, application for noise-smoothing and structure-sharpening in medical image signal arrays of the type generated and displayed by magnetic resonance imaging (MRI) systems.
2. Related Art
Digital image signals (representing the arrayed pixels of a digitized image displayable on a CRT or other form of image signal transducer) are now commonly available from many sources. In the medical imaging field, such images are typically produced by CT scanners and MRI systems. Even traditional medical X-rays initially made on photographic film can be scanned and convened to a digitized image signal format. Digital image signals typically comprise an orderly array of multiple bit digital data values where each value (e.g., "words" or "bytes") represents the intensity (and/or color) of a particular predetermined pixel in a visual image to be displayed (e.g., by raster scanning and transducing the signals into visual images).
Unfortunately, such digital image signals typically include noise components which detract from image quality in both analytic and aesthetic senses. Especially in the case of medical images, such noise artifacts are troublesome since they may lead to inaccurate medical diagnoses or to an inability to make any diagnosis based on the viewable image. Accordingly, considerable efforts have been expended to filter noise components out of digital image signals such that the resulting filtered image, when displayed, will represent a more analytically correct and aesthetically pleasing representation of the patient tissue depicted in the display.
Although many quite sophisticated image filters have been proposed and/or employed, many of them have limited applicability and/or are otherwise often rather impractical to employ in a typical clinical environment where time and equipment resources are necessarily limited (e.g., as compared to a research laboratory environment, where virtually unlimited time and equipment resources may be available for repeated attempts at image enhancement filtering). Accordingly, there remains a considerable need for adaptive digital image signal filtering techniques that are both highly efficacious and economical (in terms of both required filter processing time and required filtering equipment resources).
In the field of MRI, a two-dimensional digitized image is typically obtained from a magnetic resonance imaging system representing for medical diagnosis a cross-sectional image of NMR nuclei defining tissue structures within a three-dimensional object. Unfortunately, most MR images are degraded by various distortions. Sometimes, these distortions result in inadequate object tissue representation and make accurate image analysis difficult. Among these distortions,noise contamination is often the most serious one. Effective corrections of other distortions are often closely related to and based on successful noise reduction. Therefore, noise reduction is a central issue in MR image processing for better image quality.
Some general background concerning problems and requirements in MR image processing may be found in the following references, the content of which is hereby incorporated by reference):
F. W. Wehrli, Biomedical Magnetic Resonance Imaging--Principles, Methodology, and Applications, Edited by F. W. Wehrli, D. Shaw and J. B. Kneeland, VCH Publishers, Inc., 1988; PA1 L. Kaufman, L. E. Crooks and J. Carlson, "Technology Requirements for Magnetic Resonance Imaging System, in Proceedings of Technology Requirements for Biomedical Imaging, IEEE Computer Society Press, May 1991; PA1 A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliffs, N.J. 07632, 1989; PA1 W. K. Pratt, Digital Image Processing, John Wiley & Sons, Inc., 1978; R. C. Gonzalez and P. Wintz, Digital Image Processing, Addison-Wesley Publishing Company, 1988; PA1 J. S. Lee, "Digital Image Enhancement and Noise Filtering by Use of Local Statistics," IEEE Trans. Patt. Anal. Machine Intell., Vol. PAMI-2, No. 2, pp. 165-68, March 1980; PA1 J. W. Woods, S. Dravida and R. Mediavilla, "Image Estimation Using Doubly Stochastic Gaussian Random Field Models," IEEE Trans. Pattern. Anal. Machine Intell. Vol. PAMI-9, pp. 245-53, March 1987; PA1 L. L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis, Addison-Wesley Publishing Company, 1990; PA1 L. B. Jackson, Digital Filters and Signal Processing, Kluwer Academic Publishing, 1989.
Noise filtering of images is essentially a smoothing process, but simple low-pass filtering will typically blur image edges and other tissue structures and thus damage structural tissue image fidelity. These tissue structures are significant to human viewers. They are also very important for automatic image analysis and synthesis, such as computer vision and image registration. Some filters have been proposed to address this problem successfully such as adaptive filtering based on local image statistics (e.g., see J. S. Lee, "Digital Image Enhancement and Noise Filtering by Use of Local Statistics," IEEE Trans. Patt. Anal. Machine Intell., Vol. PAMI-2, No. 2, pp. 165-68, March 1980) and Kalman filtering techniques using multiple image models (e.g., see J. W. Woods, S. Dravida and R. Mediavilla, "Image Estimation Using Doubly Stochastic Gaussian Random Field Models," IEEE Trans. Pattern. Anal. Machine Intell. Vol. PAMI-9, pp. 245-53, March 1987). Since, for medical diagnosis imaging, a study of MR often requires a sequence of plural images and since each digital image contains a large number of arrayed pixels, the processing efficiency of filtering is a fundamental consideration in choosing filters for fast medical image procedssing. Thus, although many filters may eventually provide acceptable filtering performance, slow processing speed often limits their wide application in medical imaging.